1. Technical Field
The present invention relates generally to diagnosis of sleep disordered breathing and in particular to diagnosis of sleep disordered breathing utilizing electrocardiographic measurements. Still more particularly, the present invention relates to derivation of respiratory data from electrocardiographic measurements for determining either the presence of sleep disordered breathing causing or aggravating cardiac symptoms or the absence of sleep disordered breathing influence on cardiac symptoms due to cardiac pathology.
2. Description of the Related Art
Sleep disordered breathing is a significant problem for a large portion of the population. Sleep apnea, an intrinsic dyssomnia involving cessation of breathing during sleep and resulting in complete or partial arousal from sleep, is one of the most prevalent forms of sleep disordered breathing. Symptoms of the disorder include daytime sleepiness, fatigue or tiredness, and irritability, which may seriously impair the performance of the individual.
Sleep apnea is typically defined as the cessation of air exchange (breathing) from the nostrils or mouth lasting at least 10 seconds. Partial or complete arousal from sleep is considered a defensive mechanism most likely stimulated by rising carbon dioxide levels in the blood during the apneic event to reestablish ventilation and prevent death in the sleeping subject. Three established categorized of sleep apnea include: obstructive sleep apnea, obstruction of the upper airway; central sleep apnea, cessation of ventilatory effort; and mixed apnea, a combination of both upper airway obstruction and cessation of ventilatory effort.
Sleep disordered breathing may also take the form of a decrease in ventilation during sleep rather than a complete sleep apnea, which may result in hypercarpnea and sleep disturbance and is classified as hypopnea. Apneic events are usually quantified (or xe2x80x9cscoredxe2x80x9d) as either (1) more than 75% reduction in air flow, with or without change in oxygen saturation in the blood (SpO2), or (2) more than 50% reduction in air flow combined with a decrease of blood oxygen saturation by more than 10%. Hypopneic events may be variously quantified as: (1) either a 50% or greater reduction in air flow combined with at least a 4% reduction in blood oxygen saturation or, alternatively, a 20%-50% reduction in airflow in association with at least 2% loss of blood oxygen saturation; (2) a 50% or greater reduction in thoracic and abdominal activity; (3) a change in electromyogram (EMG) measurements accompanied by, rolling eye movements, indicating arousal; and/or (4) a change in electroencephalogram (EEG) measurements combined with a 20% decrease in air flow, independent of decrease in blood oxygen saturation.
While the distinction between apnea and hypopnea is largely one of severity, sleep disordered breathing diagnosis may entail measurement of both types of events. For example, the apnea-hypopnea index (AHI), representing a number of either apneic or hypopneic events per hour for a subject, is more commonly used than the apnea index (AI), representing only the total number of apneic events per hour for the subject. An AHI of more than 5 events per hour, regardless of severity, is usually qualified as sleep apnea. Other variables such as average duration of an event, number of apneic versus hypopneic events, and average decrease in blood oxygen saturation during events are utilized to determine the severity of the disorder.
Polygraphic monitoring, or polysomnography, the measurement of vital body signals during sleep, is the most commonly employed method of diagnosing sleep disorders, including sleep apnea. The data is collected during the patient""s normal sleeping time and is later scored (evaluated) visually. Various signals are recorded during the night to identify different sleep stages, respiratory variables, heart function, and muscle tone, all of which aid in scoring sleep disordered breathing events.
A conventional arrangement of the polygraphic monitoring instrumentation employed is depicted in FIG. 8. A polygraphic monitoring unit 802 is connected by a plurality of leads 804 to sensors attached to patient 806. Polygraphic monitoring unit 802 is capable of measuring a variety of body functions: electroencephalogram (EEG) lead 804a is employed to measure electrical brain activity; electrooculogram (EOG) lead 804b is employed to detect eye movements; airflow lead or leads 804c are employed to measure air flow signals from as many as three thermistors placed near the patient""s nostrils and mouth; electromyogram (EMG) lead 804d is employed to measure muscle tone from the patient""s chin area; electrocardiogram (ECG) leads 804e are employed to measure the heart function; and chest and abdominal band leads 804f and 804g are employed to measure thoracic and abdominal movements, respectively. Additionally, a pulse oximeter (not shown) may be employed to record blood oxygen saturation, an electrode may be placed on the tibialis anterior to monitor leg muscle activity, and a video recording of the patient may be taken utilizing infrared low light technology.
Measurements taken from polygraphic monitoring unit 802 are typically filtered and amplified, and recorded on a data acquisition system 808 such as those available from Tele-factor Corporation of West Conshohocken, Pa. The polysomnography signals are also usually digitized by an analog-to-digital converter 810 and transmitted to a data processing system 812 for processing and/or storage. Converter 810 may, for example, be a DAS 1200 series A/D converter board, available from Keithly Instruments, Inc. of Massachusetts, within data processing system 812.
Conventional polygraphic monitoring instrumentation is often uncomfortable to the patient. The instrumentation also embraces several forms of respiration monitoring. Currently, two broad categories of respiration monitoring may be identified: direct methods, such as nasal thermistors, spirometers, and pneumotachometers, measure air flow in and out of the lungs; indirect methods, which presently include whole body plethysmographs, inductance and impedance plethysmographs, and strain gauge measurement of chest and abdomen circumference, measure effects of respiration on the body. While direct methods are most accurate, they generally interfere with normal respiration. Most indirect methods, on the other hand, either lose their calibration readily or immobilize the patient (e.g., whole body plethysmograph).
Sleep disordered breathing is prevalent in individuals suffering from cardiovascular disease. ECG signals are routinely recorded in studies for patients with cardiac problems, as well as in patients having respiratory disorders, sleep disorders, and patients in intensive care units. ECG signals are therefore readily available for patients having a variety of disorders. Furthermore, millions of patients are screened each year using extended ECG monitoring (at least 24 hours), while generally their respiration is not monitoring due to the added cost and inconvenience of conventional airflow monitoring equipment.
Advances in the field of electrocardiography have rendered analysis and conditioning or ECG signals robust. Measurement of ECG signals does not interfere with normal breathing. Established technology has existed for years for measurement of the ECG in ambulatory patients. Thus, measurement of ECG signals is more comfortable and less intrusive for the patient than polygraphic monitoring. Properly attached ECG leads are less prone to error due to patient movement.
It is well-known that respiration affects ECG signals, principally as a result of chest movement. Much work has been performed to eliminate this effect from ECG signals to enhance detection of arrhythmia. Capture of chest movement induced modulation of ECG signals, however, could provide a means for extracting respiratory rhythms from ECG signals.
Derivation of respiratory rhythms from ECG signals would not require supplementary sensors. ECG-derived respiratory (EDR) signals could also provide valuable clinical information if they reveal an association between abnormal respiratory and cardiac events. Presently, however, no ECG monitoring device provides information regarding the patient""s breathing function. Thus, respiratory influence on cardiac function is not available to the attending physician when making an ECG-based diagnosis.
It would be desirable, therefore, to provide a mechanism for deriving respiratory data from ECG measurements. It would further be advantageous if the mechanism for generating EDR signals could be implemented in connection with a variety of mechanisms utilize to diagnose cardiac, respiratory, or sleep disorders.
Respiratory rhythms of a subject are derived from measured ECG signals utilizing leads placed for significant influence of chest movement on the ECG signals. The QRS pulses within ECG signals measured in two substantially orthogonal planes are located by applying a sequence of filters. The knot and period of the QRS pulses are then determined, with adjustments made during a learning phase of data sampling. The QRS pulse areas in both planes is then calculated. These pulse areas are employed to determine the angle of orientation of the depolarization wave""s mean electrical axis (MEA) at the QRS pulse locations. Cubic spline interpolation of the data points for the MEA angle provides a smooth breathing curve, which may be scored for sleep disordered breathing events. The ECG-derived respiratory (EDR) signal may be employed in lieu of airflow a measurements where such measurements are not available, or may be employed in conjunction with airflow measurements and/or measured cardiac activity data to discriminate between arrhythmias associated with disordered breathing versus those associated with cardiac malfunction, reducing misdiagnosis. The additional processing of ECG data required for derivation of respiratory rhythms may be easily automated and implemented at nominal incremental cost per unit.